Table 14 Credit scoring performance of the three models.

From: Example dependent cost sensitive learning based selective deep ensemble model for customer credit scoring

Datasets

Metrics

ECSTabNet + SRCGMDH

TabNet + ECSGMDH

ECS-SDE

GMSC

Save

0.44036(2)

0.25329(3)

0.45448(1)

AUC-PR

0.24319(2)

0.20808(3)

0.24449(1)

AUC-ROC

0.80079(2)

0.63848(3)

0.80102(1)

BS+

0.19840(1)

0.27091(3)

0.21223(2)

BS

0.15295(2)

0.15857(3)

0.14019(1)

PAKDD

Save

0.30444(2)

0.05202(3)

0.30556(1)

AUC-PR

0.23855(3)

0.27665(1)

0.25155(2)

AUC-ROC

0.59240(3)

0.59459(2)

0.60951(1)

BS+

0.18705(2)

0.42895(3)

0.18299(1)

BS

0.41916(3)

0.15244(1)

0.38199(2)

DCCC

Save

0.33076(2)

0.30172(3)

0.33307(1)

AUC-PR

0.43992(1)

0.41095(3)

0.41299(2)

AUC-ROC

0.71824(2)

0.69484(3)

0.72550(1)

BS+

0.22586(2)

0.24352(3)

0.22276(1)

BS

0.18231(3)

0.17478(2)

0.17378(1)

IEEE

Save

0.50054(2)

0.47631(3)

0.51258(1)

AUC-PR

0.47742(3)

0.56474(1)

0.50040(2)

AUC-ROC

0.85593(2)

0.81716(3)

0.86714(1)

BS+

0.37235(2)

0.37332(3)

0.36915(1)

BS

0.01440(3)

0.01409(2)

0.01314(1)

Average ranking

2.20

2.55

1.25